Web community detection system has been an active research area for decades. These detected web communities can help us to understand the structure of the underlying graph connection. These connections can then also guide product or service recommendations. Thus, valuable information may be obtained by identifying web communities from a large data set.
However, the large data set can include millions or billions of data points. Processing the data set can be computationally intensive and expensive using traditional methods.
There are a variety of algorithms designed to achieve this community detection task. However, the quick explosion of the web data render these existing algorithms impractical while analyzing a graph with millions of vertices and billions of edges. Brute force detection using only a CPU is an inefficient way for detecting the web communities.